Key result
A-LIKNet deep learning-accelerated CINE MRI matches standard imaging for EDV, ESV, SV, and EF.
Why the study?
To evaluate the diagnostic accuracy and quantitative agreement of single-breath-hold A-LIKNet deep learning-accelerated 2D cardiac CINE MRI compared with standard multi-breath-hold sequences for assessing biventricular volumes and function.
Does A-LIKNet deep learning-accelerated single breath-hold 2D cardiac CINE MRI provide comparable quantitative agreement to standard multi-breath-hold CINE sequences for assessing biventricular volumes and function?
Cross-Sectional (n=42)
No
Does A-LIKNet deep learning-accelerated single breath-hold 2D cardiac CINE MRI provide comparable quantitative agreement to standard multi-breath-hold CINE sequences for assessing biventricular volumes and function?
A-LIKNet deep learning-accelerated CINE MRI enables rapid, single breath-hold assessment of cardiac function with excellent agreement to standard multi-breath-hold imaging up to an acceleration factor of 16×.
Supports accelerated single breath-hold CINE feasibility; leaves open replacement of standard sequences pending randomized validation.
OBJECTIVES: To evaluate the diagnostic accuracy and quantitative agreement of A-LIKNet (attention-incorporated network for sharing low-rank, image, and k-space information) deep learning (DL)-accelerated 2D cardiac CINE MRI acquired in a single breath-hold, compared with standard multi-breath-hold CINE sequences, for assessing biventricular volumes and function. MATERIALS AND METHODS: In this single-center study, A-LIKNet DL-reconstructed CINE images were acquired at 3 acceleration factors (8×, 16×, and 24×) in 42 subjects using a single breath-hold protocol. Quantitative parameters, including left and right ventricular end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), ejection fraction (EF), and left ventricle myocardial mass (MM), were derived from standardized segmentation workflows. These were compared against standard multi-breath-hold CINE sequences and tested for agreement using Bland-Altman analysis and linear regression. Image quality was assessed using the coefficient of variation (CoV). RESULTS: Excellent agreement was observed between A-LIKNet DL-accelerated and standard CINE imaging for EDV, ESV, SV, and EF, with 95% limits of agreement (LoA) falling within predefined equivalence margins for nearly all parameters. No significant proportional bias was detected. Myocardial mass showed wider variability and exceeded equivalence thresholds, likely due to less distinct epicardial borders in higher acceleration. CoV values increased with higher acceleration, reflecting mild degradation in image quality, although segmentation performance remained robust across all levels. CONCLUSION: A-LIKNet deep learning accelerated CINE sequences enable rapid and reliable assessment of cardiac function with excellent agreement to standard imaging. From a clinical perspective, image quality remains acceptable up to an acceleration factor of 16×, supporting routine application, while 24× acceleration may be reserved for selected use cases requiring maximal speed.
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Kübler et al. (2025) conducted a cross-sectional in Cardiac imaging (n=42). A-LIKNet deep learning-accelerated 2D cardiac CINE MRI vs. Standard multi-breath-hold CINE sequences was evaluated on Diagnostic accuracy and quantitative agreement for biventricular volumes and function (EDV, ESV, SV, EF, MM). A-LIKNet deep learning-accelerated CINE MRI showed excellent agreement with standard imaging for EDV, ESV, SV, and EF, with 95% limits of agreement falling within predefined equivalence margins.
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